Statistical genomics and biological physics

Advances in sequencing and other high-throughput technologies have fueled the genomic revolution, providing an unprecedented amount of large-scale biological data. To extract information from such data, and to reach a deeper understanding of biological systems, requires the solution of hard inference problems, which are intractable by conventional computational tools.

Our group, established in Sept. 2011, draws inspiration from the statistical physics of disordered systems to develop novel algorithmic tools for solving large-scale optimization and inference tasks, to bring such computational methods to the full benefit of biological research.

Specific questions of our interest:

  • Statistical-physics inspired approaches to network inference
  • Biomolecular co-evolution and structure prediction
  • Inference of signaling networks


  • Statistical inference and statistical physics
  • Statistical modeling of biological data
  • Biomolecular co-evolution and structure prediction
  • Inference of signaling networks


University of California at San Diego, US (T. Hwa, B. Lunt)

The Scripps Research Institute, US (H. Szurmant, J.A. Hoch)

Rice University, Houston, US (J. Onuchic, F. Morcos)

Human Genetics Foundation, Turin, Italy (R. Zecchina, A. Pagnani, C. Baldassari)

Karlsruhe Institute for Technology (A. Schug, B. Lutz)

Ecole Normale Supérieure, Paris, France (R. Monasson, S. Cocco)

Memorial Sloan-Kettering Cancer Center (C. Sander)

INSERM (O. Tenaillon)

European Bioinformatics Institute / EMBL Cambridge (M. Punta)

Howard-Hughes Medical Institute, Janelia Farm Research Campus (S. Eddy)